Hemagglutinin Fusion Peptide Mutants in Model Membranes: Structural Properties, Membrane Physical Properties, and PEG-Mediated Fusion

While the importance of viral fusion peptides (e.g., hemagglutinin (HA) and gp41) in virus-cell membrane fusion is established, it is unclear how these peptides enhance membrane fusion, especially at low peptide/lipid ratios for which the peptides are not lytic. We assayed wild-type HA fusion peptide and two mutants, G1E and G13L, for their effects on the bilayer structure of 1,2-dioleoyl-3-sn-phosphatidylcholine/1,2-dioleoyl-3-sn-phosphatidylethanolamine/Sphingomyelin/Cholesterol (35:30:15:20) membranes, their structures in the lipid bilayer, and their effects on membrane fusion. All peptides bound to highly curved vesicles, but fusion was triggered only in the presence of poly(ethylene glycol). At low (1:200) peptide/lipid ratios, wild-type peptide enhanced remarkably the extent of content mixing and leakage along with the rate constants for these processes, and significantly enhanced the bilayer interior packing and filled the membrane free volume. The mutants caused no change in contents mixing or interior packing. Circular dichroism, polarized-attenuated total-internal-reflection Fourier-transform infrared spectroscopy measurements, and membrane perturbation measurements all conform to the inverted-V model for the structure of wild-type HA peptide. Similar measurements suggest that the G13L mutant adopts a less helical conformation in which the N-terminus moves closer to the bilayer interface, thus disrupting the V-structure. The G1E peptide barely perturbs the bilayer and may locate slightly above the interface. Fusion measurements suggest that the wild-type peptide promotes conversion of the stalk to an expanded trans-membrane contact intermediate through its ability to occupy hydrophobic space in a trans-membrane contact structure. While wild-type peptide increases the rate of initial intermediate and final pore formation, our results do not speak to the mechanisms for these effects, but they do leave open the possibility that it stabilizes the transition states for these events

The effects of user characteristics on query performance in the presence of information request ambiguity

This paper investigates the effects of personality characteristics on individuals' abilities to compose queries from information requests containing various types of ambiguity. In particular, this research examines the effects of user personality characteristics on query performance in the presence of information requests that contained no extraneous, syntactic, or both extraneous and syntactic ambiguities. The results indicate that personality characteristics significantly affect users' abilities to compose accurate queries. Neuroticism, agreeableness, openness to experience, and conscientiousness significantly affected the number of errors made in the query formulations. Conscientiousness affected the length of time taken to compose the queries and neuroticism affected the confidence users had in the accuracy of their queries. Although several personality dimensions affected query performance, no significant interactions between personality dimensions and ambiguity were detected. Furthermore, both query complexity and information request ambiguity exhibited greater impacts on query performance than personality characteristics. Hence, organizations should attempt to train users to deal with query complexity and information request ambiguity before modifying their training programs for personality characteristics

The Gene Ontology knowledgebase in 2023

The Gene Ontology (GO) knowledgebase (http://geneontology.org) is a comprehensive resource concerning the functions of genes and gene products (proteins and noncoding RNAs). GO annotations cover genes from organisms across the tree of life as well as viruses, though most gene function knowledge currently derives from experiments carried out in a relatively small number of model organisms. Here, we provide an updated overview of the GO knowledgebase, as well as the efforts of the broad, international consortium of scientists that develops, maintains, and updates the GO knowledgebase. The GO knowledgebase consists of three components: (1) the GO-a computational knowledge structure describing the functional characteristics of genes; (2) GO annotations-evidence-supported statements asserting that a specific gene product has a particular functional characteristic; and (3) GO Causal Activity Models (GO-CAMs)-mechanistic models of molecular "pathways" (GO biological processes) created by linking multiple GO annotations using defined relations. Each of these components is continually expanded, revised, and updated in response to newly published discoveries and receives extensive QA checks, reviews, and user feedback. For each of these components, we provide a description of the current contents, recent developments to keep the knowledgebase up to date with new discoveries, and guidance on how users can best make use of the data that we provide. We conclude with future directions for the project